def test_shape_aware_head_loss():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
bbox_head_cfg = _get_pts_bbox_head_cfg(
'ssn/hv_ssn_secfpn_sbn-all_2x16_2x_lyft-3d.py')
# modify bn config to avoid bugs caused by syncbn
for task in bbox_head_cfg['tasks']:
task['norm_cfg'] = dict(type='BN2d')
from mmdet3d.models.builder import build_head
self = build_head(bbox_head_cfg)
self.cuda()
assert len(self.heads) == 4
assert isinstance(self.heads[0].conv_cls, torch.nn.modules.conv.Conv2d)
assert self.heads[0].conv_cls.in_channels == 64
assert self.heads[0].conv_cls.out_channels == 36
assert self.heads[0].conv_reg.out_channels == 28
assert self.heads[0].conv_dir_cls.out_channels == 8
# test forward
feats = list()
feats.append(torch.rand([2, 384, 200, 200], dtype=torch.float32).cuda())
(cls_score, bbox_pred, dir_cls_preds) = self.forward(feats)
assert cls_score[0].shape == torch.Size([2, 420000, 9])
assert bbox_pred[0].shape == torch.Size([2, 420000, 7])
assert dir_cls_preds[0].shape == torch.Size([2, 420000, 2])
# test loss
gt_bboxes = [
LiDARInstance3DBoxes(
torch.tensor(
[[-14.5695, -6.4169, -2.1054, 1.8830, 4.6720, 1.4840, 1.5587],
[25.7215, 3.4581, -1.3456, 1.6720, 4.4090, 1.5830, 1.5301]],
dtype=torch.float32).cuda()),
LiDARInstance3DBoxes(
torch.tensor(
[[-50.763, -3.5517, -0.99658, 1.7430, 4.4020, 1.6990, 1.7874],
[-68.720, 0.033, -0.75276, 1.7860, 4.9100, 1.6610, 1.7525]],
dtype=torch.float32).cuda())
]
gt_labels = list(torch.tensor([[4, 4], [4, 4]], dtype=torch.int64).cuda())
input_metas = [{
'sample_idx': 1234
}, {
'sample_idx': 2345
}] # fake input_metas
losses = self.loss(cls_score, bbox_pred, dir_cls_preds, gt_bboxes,
gt_labels, input_metas)
assert losses['loss_cls'][0] > 0
assert losses['loss_bbox'][0] > 0
assert losses['loss_dir'][0] > 0
# test empty ground truth case
gt_bboxes = list(torch.empty((2, 0, 7)).cuda())
gt_labels = list(torch.empty((2, 0)).cuda())
empty_gt_losses = self.loss(cls_score, bbox_pred, dir_cls_preds, gt_bboxes,
gt_labels, input_metas)
assert empty_gt_losses['loss_cls'][0] > 0
assert empty_gt_losses['loss_bbox'][0] == 0
assert empty_gt_losses['loss_dir'][0] == 0
def test_dcn_center_head():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and CUDA')
set_random_seed(0)
tasks = [
dict(num_class=1, class_names=['car']),
dict(num_class=2, class_names=['truck', 'construction_vehicle']),
dict(num_class=2, class_names=['bus', 'trailer']),
dict(num_class=1, class_names=['barrier']),
dict(num_class=2, class_names=['motorcycle', 'bicycle']),
dict(num_class=2, class_names=['pedestrian', 'traffic_cone']),
]
voxel_size = [0.2, 0.2, 8]
dcn_center_head_cfg = dict(
type='CenterHead',
in_channels=sum([128, 128, 128]),
tasks=[
dict(num_class=1, class_names=['car']),
dict(num_class=2, class_names=['truck', 'construction_vehicle']),
dict(num_class=2, class_names=['bus', 'trailer']),
dict(num_class=1, class_names=['barrier']),
dict(num_class=2, class_names=['motorcycle', 'bicycle']),
dict(num_class=2, class_names=['pedestrian', 'traffic_cone']),
],
common_heads={
'reg': (2, 2),
'height': (1, 2),
'dim': (3, 2),
'rot': (2, 2),
'vel': (2, 2)
},
share_conv_channel=64,
bbox_coder=dict(
type='CenterPointBBoxCoder',
post_center_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
max_num=500,
score_threshold=0.1,
pc_range=[-51.2, -51.2],
out_size_factor=4,
voxel_size=voxel_size[:2],
code_size=9),
separate_head=dict(
type='DCNSeparateHead',
dcn_config=dict(
type='DCN',
in_channels=64,
out_channels=64,
kernel_size=3,
padding=1,
groups=4,
bias=False), # mmcv 1.2.6 doesn't support bias=True anymore
init_bias=-2.19,
final_kernel=3),
loss_cls=dict(type='GaussianFocalLoss', reduction='mean'),
loss_bbox=dict(type='L1Loss', reduction='none', loss_weight=0.25),
norm_bbox=True)
# model training and testing settings
train_cfg = dict(
grid_size=[512, 512, 1],
point_cloud_range=[-51.2, -51.2, -5., 51.2, 51.2, 3.],
voxel_size=voxel_size,
out_size_factor=4,
dense_reg=1,
gaussian_overlap=0.1,
max_objs=500,
min_radius=2,
code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2, 1.0, 1.0])
test_cfg = dict(
post_center_limit_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
max_per_img=500,
max_pool_nms=False,
min_radius=[4, 12, 10, 1, 0.85, 0.175],
post_max_size=83,
score_threshold=0.1,
pc_range=[-51.2, -51.2],
out_size_factor=4,
voxel_size=voxel_size[:2],
nms_type='circle')
dcn_center_head_cfg.update(train_cfg=train_cfg, test_cfg=test_cfg)
dcn_center_head = build_head(dcn_center_head_cfg).cuda()
x = torch.ones([2, 384, 128, 128]).cuda()
output = dcn_center_head([x])
for i in range(6):
assert output[i][0]['reg'].shape == torch.Size([2, 2, 128, 128])
assert output[i][0]['height'].shape == torch.Size([2, 1, 128, 128])
assert output[i][0]['dim'].shape == torch.Size([2, 3, 128, 128])
assert output[i][0]['rot'].shape == torch.Size([2, 2, 128, 128])
assert output[i][0]['vel'].shape == torch.Size([2, 2, 128, 128])
assert output[i][0]['heatmap'].shape == torch.Size(
[2, tasks[i]['num_class'], 128, 128])
# Test loss.
gt_bboxes_0 = LiDARInstance3DBoxes(torch.rand([10, 9]).cuda(), box_dim=9)
gt_bboxes_1 = LiDARInstance3DBoxes(torch.rand([20, 9]).cuda(), box_dim=9)
gt_labels_0 = torch.randint(1, 11, [10]).cuda()
gt_labels_1 = torch.randint(1, 11, [20]).cuda()
gt_bboxes_3d = [gt_bboxes_0, gt_bboxes_1]
gt_labels_3d = [gt_labels_0, gt_labels_1]
loss = dcn_center_head.loss(gt_bboxes_3d, gt_labels_3d, output)
for key, item in loss.items():
if 'heatmap' in key:
assert item >= 0
else:
assert torch.sum(item) >= 0
# test get_bboxes
img_metas = [
dict(box_type_3d=LiDARInstance3DBoxes),
dict(box_type_3d=LiDARInstance3DBoxes)
]
ret_lists = dcn_center_head.get_bboxes(output, img_metas)
for ret_list in ret_lists:
assert ret_list[0].tensor.shape[0] <= 500
assert ret_list[1].shape[0] <= 500
assert ret_list[2].shape[0] <= 500
def test_ssd3d_head():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
_setup_seed(0)
ssd3d_head_cfg = _get_vote_head_cfg('3dssd/3dssd_4x4_kitti-3d-car.py')
ssd3d_head_cfg.vote_module_cfg.num_points = 64
self = build_head(ssd3d_head_cfg).cuda()
sa_xyz = [torch.rand([2, 128, 3], dtype=torch.float32).cuda()]
sa_features = [torch.rand([2, 256, 128], dtype=torch.float32).cuda()]
sa_indices = [torch.randint(0, 64, [2, 128]).cuda()]
input_dict = dict(sa_xyz=sa_xyz,
sa_features=sa_features,
sa_indices=sa_indices)
# test forward
ret_dict = self(input_dict, 'spec')
assert ret_dict['center'].shape == torch.Size([2, 64, 3])
assert ret_dict['obj_scores'].shape == torch.Size([2, 1, 64])
assert ret_dict['size'].shape == torch.Size([2, 64, 3])
assert ret_dict['dir_res'].shape == torch.Size([2, 64, 12])
# test loss
points = [torch.rand([4000, 3], device='cuda') for i in range(2)]
gt_bbox1 = LiDARInstance3DBoxes(torch.rand([5, 7], device='cuda'))
gt_bbox2 = LiDARInstance3DBoxes(torch.rand([5, 7], device='cuda'))
gt_bboxes = [gt_bbox1, gt_bbox2]
gt_labels = [
torch.zeros([5], dtype=torch.long, device='cuda') for i in range(2)
]
img_metas = [dict(box_type_3d=LiDARInstance3DBoxes) for i in range(2)]
losses = self.loss(ret_dict,
points,
gt_bboxes,
gt_labels,
img_metas=img_metas)
assert losses['centerness_loss'] >= 0
assert losses['center_loss'] >= 0
assert losses['dir_class_loss'] >= 0
assert losses['dir_res_loss'] >= 0
assert losses['size_res_loss'] >= 0
assert losses['corner_loss'] >= 0
assert losses['vote_loss'] >= 0
# test multiclass_nms_single
sem_scores = ret_dict['obj_scores'].transpose(1, 2)[0]
obj_scores = sem_scores.max(-1)[0]
bbox = self.bbox_coder.decode(ret_dict)[0]
input_meta = img_metas[0]
bbox_selected, score_selected, labels = self.multiclass_nms_single(
obj_scores, sem_scores, bbox, points[0], input_meta)
assert bbox_selected.shape[0] >= 0
assert bbox_selected.shape[1] == 7
assert score_selected.shape[0] >= 0
assert labels.shape[0] >= 0
# test get_boxes
points = torch.stack(points, 0)
results = self.get_bboxes(points, ret_dict, img_metas)
assert results[0][0].tensor.shape[0] >= 0
assert results[0][0].tensor.shape[1] == 7
assert results[0][1].shape[0] >= 0
assert results[0][2].shape[0] >= 0
def test_h3d_head():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
_setup_seed(0)
h3d_head_cfg = _get_roi_head_cfg('h3dnet/h3dnet_3x8_scannet-3d-18class.py')
num_point = 128
num_proposal = 64
h3d_head_cfg.primitive_list[0].vote_aggregation_cfg.num_point = num_point
h3d_head_cfg.primitive_list[1].vote_aggregation_cfg.num_point = num_point
h3d_head_cfg.primitive_list[2].vote_aggregation_cfg.num_point = num_point
h3d_head_cfg.bbox_head.num_proposal = num_proposal
self = build_head(h3d_head_cfg).cuda()
# prepare roi outputs
fp_xyz = [torch.rand([1, num_point, 3], dtype=torch.float32).cuda()]
hd_features = torch.rand([1, 256, num_point], dtype=torch.float32).cuda()
fp_indices = [torch.randint(0, 128, [1, num_point]).cuda()]
aggregated_points = torch.rand([1, num_proposal, 3],
dtype=torch.float32).cuda()
aggregated_features = torch.rand([1, 128, num_proposal],
dtype=torch.float32).cuda()
proposal_list = torch.cat([
torch.rand([1, num_proposal, 3], dtype=torch.float32).cuda() * 4 - 2,
torch.rand([1, num_proposal, 3], dtype=torch.float32).cuda() * 4,
torch.zeros([1, num_proposal, 1]).cuda()
],
dim=-1)
input_dict = dict(fp_xyz_net0=fp_xyz,
hd_feature=hd_features,
aggregated_points=aggregated_points,
aggregated_features=aggregated_features,
seed_points=fp_xyz[0],
seed_indices=fp_indices[0],
proposal_list=proposal_list)
# prepare gt label
from mmdet3d.core.bbox import DepthInstance3DBoxes
gt_bboxes_3d = [
DepthInstance3DBoxes(torch.rand([4, 7], dtype=torch.float32).cuda()),
DepthInstance3DBoxes(torch.rand([4, 7], dtype=torch.float32).cuda())
]
gt_labels_3d = torch.randint(0, 18, [1, 4]).cuda()
gt_labels_3d = [gt_labels_3d[0]]
pts_semantic_mask = torch.randint(0, 19, [1, num_point]).cuda()
pts_semantic_mask = [pts_semantic_mask[0]]
pts_instance_mask = torch.randint(0, 4, [1, num_point]).cuda()
pts_instance_mask = [pts_instance_mask[0]]
points = torch.rand([1, num_point, 3], dtype=torch.float32).cuda()
# prepare rpn targets
vote_targets = torch.rand([1, num_point, 9], dtype=torch.float32).cuda()
vote_target_masks = torch.rand([1, num_point], dtype=torch.float32).cuda()
size_class_targets = torch.rand([1, num_proposal],
dtype=torch.float32).cuda().long()
size_res_targets = torch.rand([1, num_proposal, 3],
dtype=torch.float32).cuda()
dir_class_targets = torch.rand([1, num_proposal],
dtype=torch.float32).cuda().long()
dir_res_targets = torch.rand([1, num_proposal], dtype=torch.float32).cuda()
center_targets = torch.rand([1, 4, 3], dtype=torch.float32).cuda()
mask_targets = torch.rand([1, num_proposal],
dtype=torch.float32).cuda().long()
valid_gt_masks = torch.rand([1, 4], dtype=torch.float32).cuda()
objectness_targets = torch.rand([1, num_proposal],
dtype=torch.float32).cuda().long()
objectness_weights = torch.rand([1, num_proposal],
dtype=torch.float32).cuda()
box_loss_weights = torch.rand([1, num_proposal],
dtype=torch.float32).cuda()
valid_gt_weights = torch.rand([1, 4], dtype=torch.float32).cuda()
targets = (vote_targets, vote_target_masks, size_class_targets,
size_res_targets, dir_class_targets, dir_res_targets,
center_targets, None, mask_targets, valid_gt_masks,
objectness_targets, objectness_weights, box_loss_weights,
valid_gt_weights)
input_dict['targets'] = targets
# train forward
ret_dict = self.forward_train(input_dict,
points=points,
gt_bboxes_3d=gt_bboxes_3d,
gt_labels_3d=gt_labels_3d,
pts_semantic_mask=pts_semantic_mask,
pts_instance_mask=pts_instance_mask,
img_metas=None)
assert ret_dict['flag_loss_z'] >= 0
assert ret_dict['vote_loss_z'] >= 0
assert ret_dict['center_loss_z'] >= 0
assert ret_dict['size_loss_z'] >= 0
assert ret_dict['sem_loss_z'] >= 0
assert ret_dict['objectness_loss_optimized'] >= 0
assert ret_dict['primitive_sem_matching_loss'] >= 0
def test_center_head():
tasks = [
dict(num_class=1, class_names=['car']),
dict(num_class=2, class_names=['truck', 'construction_vehicle']),
dict(num_class=2, class_names=['bus', 'trailer']),
dict(num_class=1, class_names=['barrier']),
dict(num_class=2, class_names=['motorcycle', 'bicycle']),
dict(num_class=2, class_names=['pedestrian', 'traffic_cone']),
]
bbox_cfg = dict(type='CenterPointBBoxCoder',
post_center_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
max_num=500,
score_threshold=0.1,
pc_range=[-51.2, -51.2],
out_size_factor=8,
voxel_size=[0.2, 0.2])
train_cfg = dict(
grid_size=[1024, 1024, 40],
point_cloud_range=[-51.2, -51.2, -5., 51.2, 51.2, 3.],
voxel_size=[0.1, 0.1, 0.2],
out_size_factor=8,
dense_reg=1,
gaussian_overlap=0.1,
max_objs=500,
code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2, 1.0, 1.0],
min_radius=2)
test_cfg = dict(
post_center_limit_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
max_per_img=500,
max_pool_nms=False,
min_radius=[4, 12, 10, 1, 0.85, 0.175],
post_max_size=83,
score_threshold=0.1,
pc_range=[-51.2, -51.2],
out_size_factor=8,
voxel_size=[0.2, 0.2],
nms_type='circle')
center_head_cfg = dict(type='CenterHead',
in_channels=sum([256, 256]),
tasks=tasks,
train_cfg=train_cfg,
test_cfg=test_cfg,
bbox_coder=bbox_cfg,
common_heads=dict(reg=(2, 2),
height=(1, 2),
dim=(3, 2),
rot=(2, 2),
vel=(2, 2)),
share_conv_channel=64,
norm_bbox=True)
center_head = build_head(center_head_cfg)
x = torch.rand([2, 512, 128, 128])
output = center_head([x])
for i in range(6):
assert output[i][0]['reg'].shape == torch.Size([2, 2, 128, 128])
assert output[i][0]['height'].shape == torch.Size([2, 1, 128, 128])
assert output[i][0]['dim'].shape == torch.Size([2, 3, 128, 128])
assert output[i][0]['rot'].shape == torch.Size([2, 2, 128, 128])
assert output[i][0]['vel'].shape == torch.Size([2, 2, 128, 128])
assert output[i][0]['heatmap'].shape == torch.Size(
[2, tasks[i]['num_class'], 128, 128])
# test get_bboxes
img_metas = [
dict(box_type_3d=LiDARInstance3DBoxes),
dict(box_type_3d=LiDARInstance3DBoxes)
]
ret_lists = center_head.get_bboxes(output, img_metas)
for ret_list in ret_lists:
assert ret_list[0].tensor.shape[0] <= 500
assert ret_list[1].shape[0] <= 500
assert ret_list[2].shape[0] <= 500
def test_part_aggregation_ROI_head():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
roi_head_cfg = _get_roi_head_cfg(
'parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class.py')
self = build_head(roi_head_cfg).cuda()
features = np.load('./tests/test_samples/parta2_roihead_inputs.npz')
seg_features = torch.tensor(features['seg_features'],
dtype=torch.float32,
device='cuda')
feats_dict = dict(seg_features=seg_features)
voxels = torch.tensor(features['voxels'],
dtype=torch.float32,
device='cuda')
num_points = torch.ones([500], device='cuda')
coors = torch.zeros([500, 4], device='cuda')
voxel_centers = torch.zeros([500, 3], device='cuda')
box_type_3d = LiDARInstance3DBoxes
img_metas = [dict(box_type_3d=box_type_3d)]
voxels_dict = dict(voxels=voxels,
num_points=num_points,
coors=coors,
voxel_centers=voxel_centers)
pred_bboxes = LiDARInstance3DBoxes(
torch.tensor(
[[0.3990, 0.5167, 0.0249, 0.9401, 0.9459, 0.7967, 0.4150],
[0.8203, 0.2290, 0.9096, 0.1183, 0.0752, 0.4092, 0.9601],
[0.2093, 0.1940, 0.8909, 0.4387, 0.3570, 0.5454, 0.8299],
[0.2099, 0.7684, 0.4290, 0.2117, 0.6606, 0.1654, 0.4250],
[0.9927, 0.6964, 0.2472, 0.7028, 0.7494, 0.9303, 0.0494]],
dtype=torch.float32,
device='cuda'))
pred_scores = torch.tensor([0.9722, 0.7910, 0.4690, 0.3300, 0.3345],
dtype=torch.float32,
device='cuda')
pred_labels = torch.tensor([0, 1, 0, 2, 1],
dtype=torch.int64,
device='cuda')
pred_clses = torch.tensor(
[[0.7874, 0.1344, 0.2190], [0.8193, 0.6969, 0.7304],
[0.2328, 0.9028, 0.3900], [0.6177, 0.5012, 0.2330],
[0.8985, 0.4894, 0.7152]],
dtype=torch.float32,
device='cuda')
proposal = dict(boxes_3d=pred_bboxes,
scores_3d=pred_scores,
labels_3d=pred_labels,
cls_preds=pred_clses)
proposal_list = [proposal]
gt_bboxes_3d = [LiDARInstance3DBoxes(torch.rand([5, 7], device='cuda'))]
gt_labels_3d = [torch.randint(0, 3, [5], device='cuda')]
losses = self.forward_train(feats_dict, voxels_dict, {}, proposal_list,
gt_bboxes_3d, gt_labels_3d)
assert losses['loss_seg'] >= 0
assert losses['loss_part'] >= 0
assert losses['loss_cls'] >= 0
assert losses['loss_bbox'] >= 0
assert losses['loss_corner'] >= 0
bbox_results = self.simple_test(feats_dict, voxels_dict, img_metas,
proposal_list)
boxes_3d = bbox_results[0]['boxes_3d']
scores_3d = bbox_results[0]['scores_3d']
labels_3d = bbox_results[0]['labels_3d']
assert boxes_3d.tensor.shape == (12, 7)
assert scores_3d.shape == (12, )
assert labels_3d.shape == (12, )
def test_primitive_head():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
_setup_seed(0)
primitive_head_cfg = dict(
type='PrimitiveHead',
num_dims=2,
num_classes=18,
primitive_mode='z',
vote_module_cfg=dict(in_channels=256,
vote_per_seed=1,
gt_per_seed=1,
conv_channels=(256, 256),
conv_cfg=dict(type='Conv1d'),
norm_cfg=dict(type='BN1d'),
norm_feats=True,
vote_loss=dict(type='ChamferDistance',
mode='l1',
reduction='none',
loss_dst_weight=10.0)),
vote_aggregation_cfg=dict(type='PointSAModule',
num_point=64,
radius=0.3,
num_sample=16,
mlp_channels=[256, 128, 128, 128],
use_xyz=True,
normalize_xyz=True),
feat_channels=(128, 128),
conv_cfg=dict(type='Conv1d'),
norm_cfg=dict(type='BN1d'),
objectness_loss=dict(type='CrossEntropyLoss',
class_weight=[0.4, 0.6],
reduction='mean',
loss_weight=1.0),
center_loss=dict(type='ChamferDistance',
mode='l1',
reduction='sum',
loss_src_weight=1.0,
loss_dst_weight=1.0),
semantic_reg_loss=dict(type='ChamferDistance',
mode='l1',
reduction='sum',
loss_src_weight=1.0,
loss_dst_weight=1.0),
semantic_cls_loss=dict(type='CrossEntropyLoss',
reduction='sum',
loss_weight=1.0),
train_cfg=dict(dist_thresh=0.2,
var_thresh=1e-2,
lower_thresh=1e-6,
num_point=100,
num_point_line=10,
line_thresh=0.2))
self = build_head(primitive_head_cfg).cuda()
fp_xyz = [torch.rand([2, 64, 3], dtype=torch.float32).cuda()]
hd_features = torch.rand([2, 256, 64], dtype=torch.float32).cuda()
fp_indices = [torch.randint(0, 64, [2, 64]).cuda()]
input_dict = dict(fp_xyz_net0=fp_xyz,
hd_feature=hd_features,
fp_indices_net0=fp_indices)
# test forward
ret_dict = self(input_dict, 'vote')
assert ret_dict['center_z'].shape == torch.Size([2, 64, 3])
assert ret_dict['size_residuals_z'].shape == torch.Size([2, 64, 2])
assert ret_dict['sem_cls_scores_z'].shape == torch.Size([2, 64, 18])
assert ret_dict['aggregated_points_z'].shape == torch.Size([2, 64, 3])
# test loss
points = torch.rand([2, 1024, 3], dtype=torch.float32).cuda()
ret_dict['seed_points'] = fp_xyz[0]
ret_dict['seed_indices'] = fp_indices[0]
from mmdet3d.core.bbox import DepthInstance3DBoxes
gt_bboxes_3d = [
DepthInstance3DBoxes(torch.rand([4, 7], dtype=torch.float32).cuda()),
DepthInstance3DBoxes(torch.rand([4, 7], dtype=torch.float32).cuda())
]
gt_labels_3d = torch.randint(0, 18, [2, 4]).cuda()
gt_labels_3d = [gt_labels_3d[0], gt_labels_3d[1]]
pts_semantic_mask = torch.randint(0, 19, [2, 1024]).cuda()
pts_semantic_mask = [pts_semantic_mask[0], pts_semantic_mask[1]]
pts_instance_mask = torch.randint(0, 4, [2, 1024]).cuda()
pts_instance_mask = [pts_instance_mask[0], pts_instance_mask[1]]
loss_input_dict = dict(bbox_preds=ret_dict,
points=points,
gt_bboxes_3d=gt_bboxes_3d,
gt_labels_3d=gt_labels_3d,
pts_semantic_mask=pts_semantic_mask,
pts_instance_mask=pts_instance_mask)
losses_dict = self.loss(**loss_input_dict)
assert losses_dict['flag_loss_z'] >= 0
assert losses_dict['vote_loss_z'] >= 0
assert losses_dict['center_loss_z'] >= 0
assert losses_dict['size_loss_z'] >= 0
assert losses_dict['sem_loss_z'] >= 0
# 'Primitive_mode' should be one of ['z', 'xy', 'line']
with pytest.raises(AssertionError):
primitive_head_cfg['vote_module_cfg']['in_channels'] = 'xyz'
build_head(primitive_head_cfg)
def test_vote_head():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
_setup_seed(0)
vote_head_cfg = _get_vote_head_cfg(
'votenet/votenet_8x8_scannet-3d-18class.py')
self = build_head(vote_head_cfg).cuda()
fp_xyz = [torch.rand([2, 256, 3], dtype=torch.float32).cuda()]
fp_features = [torch.rand([2, 256, 256], dtype=torch.float32).cuda()]
fp_indices = [torch.randint(0, 128, [2, 256]).cuda()]
input_dict = dict(fp_xyz=fp_xyz,
fp_features=fp_features,
fp_indices=fp_indices)
# test forward
ret_dict = self(input_dict, 'vote')
assert ret_dict['center'].shape == torch.Size([2, 256, 3])
assert ret_dict['obj_scores'].shape == torch.Size([2, 256, 2])
assert ret_dict['size_res'].shape == torch.Size([2, 256, 18, 3])
assert ret_dict['dir_res'].shape == torch.Size([2, 256, 1])
# test loss
points = [torch.rand([40000, 4], device='cuda') for i in range(2)]
gt_bbox1 = LiDARInstance3DBoxes(torch.rand([10, 7], device='cuda'))
gt_bbox2 = LiDARInstance3DBoxes(torch.rand([10, 7], device='cuda'))
gt_bboxes = [gt_bbox1, gt_bbox2]
gt_labels = [torch.randint(0, 18, [10], device='cuda') for i in range(2)]
pts_semantic_mask = [
torch.randint(0, 18, [40000], device='cuda') for i in range(2)
]
pts_instance_mask = [
torch.randint(0, 10, [40000], device='cuda') for i in range(2)
]
losses = self.loss(ret_dict, points, gt_bboxes, gt_labels,
pts_semantic_mask, pts_instance_mask)
assert losses['vote_loss'] >= 0
assert losses['objectness_loss'] >= 0
assert losses['semantic_loss'] >= 0
assert losses['center_loss'] >= 0
assert losses['dir_class_loss'] >= 0
assert losses['dir_res_loss'] >= 0
assert losses['size_class_loss'] >= 0
assert losses['size_res_loss'] >= 0
# test multiclass_nms_single
obj_scores = torch.rand([256], device='cuda')
sem_scores = torch.rand([256, 18], device='cuda')
points = torch.rand([40000, 3], device='cuda')
bbox = torch.rand([256, 7], device='cuda')
input_meta = dict(box_type_3d=DepthInstance3DBoxes)
bbox_selected, score_selected, labels = self.multiclass_nms_single(
obj_scores, sem_scores, bbox, points, input_meta)
assert bbox_selected.shape[0] >= 0
assert bbox_selected.shape[1] == 7
assert score_selected.shape[0] >= 0
assert labels.shape[0] >= 0
# test get_boxes
points = torch.rand([1, 40000, 4], device='cuda')
seed_points = torch.rand([1, 1024, 3], device='cuda')
seed_indices = torch.randint(0, 40000, [1, 1024], device='cuda')
vote_points = torch.rand([1, 1024, 3], device='cuda')
vote_features = torch.rand([1, 256, 1024], device='cuda')
aggregated_points = torch.rand([1, 256, 3], device='cuda')
aggregated_indices = torch.range(0, 256, device='cuda')
obj_scores = torch.rand([1, 256, 2], device='cuda')
center = torch.rand([1, 256, 3], device='cuda')
dir_class = torch.rand([1, 256, 1], device='cuda')
dir_res_norm = torch.rand([1, 256, 1], device='cuda')
dir_res = torch.rand([1, 256, 1], device='cuda')
size_class = torch.rand([1, 256, 18], device='cuda')
size_res = torch.rand([1, 256, 18, 3], device='cuda')
sem_scores = torch.rand([1, 256, 18], device='cuda')
bbox_preds = dict(seed_points=seed_points,
seed_indices=seed_indices,
vote_points=vote_points,
vote_features=vote_features,
aggregated_points=aggregated_points,
aggregated_indices=aggregated_indices,
obj_scores=obj_scores,
center=center,
dir_class=dir_class,
dir_res_norm=dir_res_norm,
dir_res=dir_res,
size_class=size_class,
size_res=size_res,
sem_scores=sem_scores)
results = self.get_bboxes(points, bbox_preds, [input_meta])
assert results[0][0].tensor.shape[0] >= 0
assert results[0][0].tensor.shape[1] == 7
assert results[0][1].shape[0] >= 0
assert results[0][2].shape[0] >= 0
def test_groupfree3d_head():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
_setup_seed(0)
vote_head_cfg = _get_vote_head_cfg(
'groupfree3d/groupfree3d_8x4_scannet-3d-18class-L6-O256.py')
self = build_head(vote_head_cfg).cuda()
fp_xyz = [torch.rand([2, 256, 3], dtype=torch.float32).cuda()]
fp_features = [torch.rand([2, 288, 256], dtype=torch.float32).cuda()]
fp_indices = [torch.randint(0, 128, [2, 256]).cuda()]
input_dict = dict(fp_xyz=fp_xyz,
fp_features=fp_features,
fp_indices=fp_indices)
# test forward
ret_dict = self(input_dict, 'kps')
assert ret_dict['seeds_obj_cls_logits'].shape == torch.Size([2, 1, 256])
assert ret_dict['s5.center'].shape == torch.Size([2, 256, 3])
assert ret_dict['s5.dir_class'].shape == torch.Size([2, 256, 1])
assert ret_dict['s5.dir_res'].shape == torch.Size([2, 256, 1])
assert ret_dict['s5.size_class'].shape == torch.Size([2, 256, 18])
assert ret_dict['s5.size_res'].shape == torch.Size([2, 256, 18, 3])
assert ret_dict['s5.obj_scores'].shape == torch.Size([2, 256, 1])
assert ret_dict['s5.sem_scores'].shape == torch.Size([2, 256, 18])
# test losses
points = [torch.rand([50000, 4], device='cuda') for i in range(2)]
gt_bbox1 = torch.rand([10, 7], dtype=torch.float32).cuda()
gt_bbox2 = torch.rand([10, 7], dtype=torch.float32).cuda()
gt_bbox1 = DepthInstance3DBoxes(gt_bbox1)
gt_bbox2 = DepthInstance3DBoxes(gt_bbox2)
gt_bboxes = [gt_bbox1, gt_bbox2]
pts_instance_mask_1 = torch.randint(0, 10, [50000], device='cuda')
pts_instance_mask_2 = torch.randint(0, 10, [50000], device='cuda')
pts_instance_mask = [pts_instance_mask_1, pts_instance_mask_2]
pts_semantic_mask_1 = torch.randint(0, 19, [50000], device='cuda')
pts_semantic_mask_2 = torch.randint(0, 19, [50000], device='cuda')
pts_semantic_mask = [pts_semantic_mask_1, pts_semantic_mask_2]
labels_1 = torch.randint(0, 18, [10], device='cuda')
labels_2 = torch.randint(0, 18, [10], device='cuda')
gt_labels = [labels_1, labels_2]
losses = self.loss(ret_dict, points, gt_bboxes, gt_labels,
pts_semantic_mask, pts_instance_mask)
assert losses['s5.objectness_loss'] >= 0
assert losses['s5.semantic_loss'] >= 0
assert losses['s5.center_loss'] >= 0
assert losses['s5.dir_class_loss'] >= 0
assert losses['s5.dir_res_loss'] >= 0
assert losses['s5.size_class_loss'] >= 0
assert losses['s5.size_res_loss'] >= 0
# test multiclass_nms_single
obj_scores = torch.rand([256], device='cuda')
sem_scores = torch.rand([256, 18], device='cuda')
points = torch.rand([50000, 3], device='cuda')
bbox = torch.rand([256, 7], device='cuda')
input_meta = dict(box_type_3d=DepthInstance3DBoxes)
bbox_selected, score_selected, labels = \
self.multiclass_nms_single(obj_scores,
sem_scores,
bbox,
points,
input_meta)
assert bbox_selected.shape[0] >= 0
assert bbox_selected.shape[1] == 7
assert score_selected.shape[0] >= 0
assert labels.shape[0] >= 0
# test get_boxes
points = torch.rand([1, 50000, 3], device='cuda')
seed_points = torch.rand([1, 1024, 3], device='cuda')
seed_indices = torch.randint(0, 50000, [1, 1024], device='cuda')
obj_scores = torch.rand([1, 256, 1], device='cuda')
center = torch.rand([1, 256, 3], device='cuda')
dir_class = torch.rand([1, 256, 1], device='cuda')
dir_res_norm = torch.rand([1, 256, 1], device='cuda')
dir_res = torch.rand([1, 256, 1], device='cuda')
size_class = torch.rand([1, 256, 18], device='cuda')
size_res = torch.rand([1, 256, 18, 3], device='cuda')
sem_scores = torch.rand([1, 256, 18], device='cuda')
bbox_preds = dict()
bbox_preds['seed_points'] = seed_points
bbox_preds['seed_indices'] = seed_indices
bbox_preds['s5.obj_scores'] = obj_scores
bbox_preds['s5.center'] = center
bbox_preds['s5.dir_class'] = dir_class
bbox_preds['s5.dir_res_norm'] = dir_res_norm
bbox_preds['s5.dir_res'] = dir_res
bbox_preds['s5.size_class'] = size_class
bbox_preds['s5.size_res'] = size_res
bbox_preds['s5.sem_scores'] = sem_scores
self.test_cfg['prediction_stages'] = 'last'
results = self.get_bboxes(points, bbox_preds, [input_meta])
assert results[0][0].tensor.shape[0] >= 0
assert results[0][0].tensor.shape[1] == 7
assert results[0][1].shape[0] >= 0
assert results[0][2].shape[0] >= 0
def test_fcos_mono3d_head():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
_setup_seed(0)
fcos3d_head_cfg = _get_head_cfg(
'fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d.py')
self = build_head(fcos3d_head_cfg).cuda()
feats = [
torch.rand([2, 256, 116, 200], dtype=torch.float32).cuda(),
torch.rand([2, 256, 58, 100], dtype=torch.float32).cuda(),
torch.rand([2, 256, 29, 50], dtype=torch.float32).cuda(),
torch.rand([2, 256, 15, 25], dtype=torch.float32).cuda(),
torch.rand([2, 256, 8, 13], dtype=torch.float32).cuda()
]
# test forward
ret_dict = self(feats)
assert len(ret_dict) == 5
assert len(ret_dict[0]) == 5
assert ret_dict[0][0].shape == torch.Size([2, 10, 116, 200])
# test loss
gt_bboxes = [
torch.rand([3, 4], dtype=torch.float32).cuda(),
torch.rand([3, 4], dtype=torch.float32).cuda()
]
gt_bboxes_3d = CameraInstance3DBoxes(torch.rand([3, 9], device='cuda'),
box_dim=9)
gt_labels = [torch.randint(0, 10, [3], device='cuda') for i in range(2)]
gt_labels_3d = gt_labels
centers2d = [
torch.rand([3, 2], dtype=torch.float32).cuda(),
torch.rand([3, 2], dtype=torch.float32).cuda()
]
depths = [
torch.rand([3], dtype=torch.float32).cuda(),
torch.rand([3], dtype=torch.float32).cuda()
]
attr_labels = [torch.randint(0, 9, [3], device='cuda') for i in range(2)]
img_metas = [
dict(cam2img=[[1260.8474446004698, 0.0, 807.968244525554],
[0.0, 1260.8474446004698, 495.3344268742088],
[0.0, 0.0, 1.0]],
scale_factor=np.array([1., 1., 1., 1.], dtype=np.float32),
box_type_3d=CameraInstance3DBoxes) for i in range(2)
]
losses = self.loss(*ret_dict, gt_bboxes, gt_labels, gt_bboxes_3d,
gt_labels_3d, centers2d, depths, attr_labels, img_metas)
assert losses['loss_cls'] >= 0
assert losses['loss_offset'] >= 0
assert losses['loss_depth'] >= 0
assert losses['loss_size'] >= 0
assert losses['loss_rotsin'] >= 0
assert losses['loss_centerness'] >= 0
assert losses['loss_velo'] >= 0
assert losses['loss_dir'] >= 0
assert losses['loss_attr'] >= 0
# test get_boxes
results = self.get_bboxes(*ret_dict, img_metas)
assert len(results) == 2
assert len(results[0]) == 4
assert results[0][0].tensor.shape == torch.Size([200, 9])
assert results[0][1].shape == torch.Size([200])
assert results[0][2].shape == torch.Size([200])
assert results[0][3].shape == torch.Size([200])
def test_PointwiseSemanticHead():
# PointwiseSemanticHead only support gpu version currently.
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
from mmdet3d.models.builder import build_head
head_cfg = dict(
type='PointwiseSemanticHead',
in_channels=8,
extra_width=0.2,
seg_score_thr=0.3,
num_classes=3,
loss_seg=dict(
type='FocalLoss',
use_sigmoid=True,
reduction='sum',
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_part=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0))
self = build_head(head_cfg)
self.cuda()
# test forward
voxel_features = torch.rand([4, 8], dtype=torch.float32).cuda()
feats_dict = self.forward(voxel_features)
assert feats_dict['seg_preds'].shape == torch.Size(
[voxel_features.shape[0], 1])
assert feats_dict['part_preds'].shape == torch.Size(
[voxel_features.shape[0], 3])
assert feats_dict['part_feats'].shape == torch.Size(
[voxel_features.shape[0], 4])
voxel_centers = torch.tensor(
[[6.56126, 0.9648336, -1.7339306], [6.8162713, -2.480431, -1.3616394],
[11.643568, -4.744306, -1.3580885], [23.482342, 6.5036807, 0.5806964]
],
dtype=torch.float32).cuda() # n, point_features
coordinates = torch.tensor(
[[0, 12, 819, 131], [0, 16, 750, 136], [1, 16, 705, 232],
[1, 35, 930, 469]],
dtype=torch.int32).cuda() # n, 4(batch, ind_x, ind_y, ind_z)
voxel_dict = dict(voxel_centers=voxel_centers, coors=coordinates)
gt_bboxes = [
LiDARInstance3DBoxes(
torch.tensor(
[[6.4118, -3.4305, -1.7291, 1.7033, 3.4693, 1.6197, -0.9091]],
dtype=torch.float32).cuda()),
LiDARInstance3DBoxes(
torch.tensor(
[[16.9107, 9.7925, -1.9201, 1.6097, 3.2786, 1.5307, -2.4056]],
dtype=torch.float32).cuda())
]
# batch size is 2 in the unit test
gt_labels = list(torch.tensor([[0], [1]], dtype=torch.int64).cuda())
# test get_targets
target_dict = self.get_targets(voxel_dict, gt_bboxes, gt_labels)
assert target_dict['seg_targets'].shape == torch.Size(
[voxel_features.shape[0]])
assert torch.allclose(target_dict['seg_targets'],
target_dict['seg_targets'].new_tensor([3, -1, 3, 3]))
assert target_dict['part_targets'].shape == torch.Size(
[voxel_features.shape[0], 3])
assert target_dict['part_targets'].sum() == 0
# test loss
loss_dict = self.loss(feats_dict, target_dict)
assert loss_dict['loss_seg'] > 0
assert loss_dict['loss_part'] == 0 # no points in gt_boxes
total_loss = loss_dict['loss_seg'] + loss_dict['loss_part']
total_loss.backward()
def test_paconv_decode_head_loss():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
paconv_decode_head_cfg = dict(type='PAConvHead',
fp_channels=((768, 256, 256), (384, 256,
256),
(320, 256, 128), (128 + 6, 128,
128, 128)),
channels=128,
num_classes=20,
dropout_ratio=0.5,
conv_cfg=dict(type='Conv1d'),
norm_cfg=dict(type='BN1d'),
act_cfg=dict(type='ReLU'),
loss_decode=dict(type='CrossEntropyLoss',
use_sigmoid=False,
class_weight=None,
loss_weight=1.0),
ignore_index=20)
self = build_head(paconv_decode_head_cfg)
self.cuda()
assert isinstance(self.conv_seg, torch.nn.Conv1d)
assert self.conv_seg.in_channels == 128
assert self.conv_seg.out_channels == 20
assert self.conv_seg.kernel_size == (1, )
assert isinstance(self.pre_seg_conv, ConvModule)
assert isinstance(self.pre_seg_conv.conv, torch.nn.Conv1d)
assert self.pre_seg_conv.conv.in_channels == 128
assert self.pre_seg_conv.conv.out_channels == 128
assert self.pre_seg_conv.conv.kernel_size == (1, )
assert isinstance(self.pre_seg_conv.bn, torch.nn.BatchNorm1d)
assert self.pre_seg_conv.bn.num_features == 128
assert isinstance(self.pre_seg_conv.activate, torch.nn.ReLU)
# test forward
sa_xyz = [
torch.rand(2, 4096, 3).float().cuda(),
torch.rand(2, 1024, 3).float().cuda(),
torch.rand(2, 256, 3).float().cuda(),
torch.rand(2, 64, 3).float().cuda(),
torch.rand(2, 16, 3).float().cuda(),
]
sa_features = [
torch.rand(2, 6, 4096).float().cuda(),
torch.rand(2, 64, 1024).float().cuda(),
torch.rand(2, 128, 256).float().cuda(),
torch.rand(2, 256, 64).float().cuda(),
torch.rand(2, 512, 16).float().cuda(),
]
input_dict = dict(sa_xyz=sa_xyz, sa_features=sa_features)
seg_logits = self(input_dict)
assert seg_logits.shape == torch.Size([2, 20, 4096])
# test loss
pts_semantic_mask = torch.randint(0, 20, (2, 4096)).long().cuda()
losses = self.losses(seg_logits, pts_semantic_mask)
assert losses['loss_sem_seg'].item() > 0
# test loss with ignore_index
ignore_index_mask = torch.ones_like(pts_semantic_mask) * 20
losses = self.losses(seg_logits, ignore_index_mask)
assert losses['loss_sem_seg'].item() == 0
# test loss with class_weight
paconv_decode_head_cfg['loss_decode'] = dict(
type='CrossEntropyLoss',
use_sigmoid=False,
class_weight=np.random.rand(20),
loss_weight=1.0)
self = build_head(paconv_decode_head_cfg)
self.cuda()
losses = self.losses(seg_logits, pts_semantic_mask)
assert losses['loss_sem_seg'].item() > 0
